Project Overview
Total Students Analyzed
4,274
Dataset Dropout Rate
32.9%
U.S. National Dropout Rate
39%
NY Non-Completers Growth
+2.3%
1.9M individuals
Background
While high school dropout rates are declining, a staggering 32.9%[1] of U.S. college students still fail to graduate. The core priority for education leaders lies in bridging these gaps by identifying the one-in-four students currently at risk of dropping out before they lose momentum.
In New York alone, there are over 1.9 million individuals with some college but no credential, a population that grew by 2.3%[2] in the past year.
Our Mission
This app aims to reduce academic failure by using machine learning to identify at-risk students at an early stage. By providing actionable insights, we enable educators to implement timely support strategies that keep students on the path to success.
Framework & Solutions
We provide a systematic approach to student success across three dimensions:
Data Reference
This project utilizes a dataset from the UCI Machine Learning Repository to present these real-world challenges. Originally created to help reduce academic attrition in higher education, this data allows us to demonstrate how machine learning can effectively flag students at risk during their academic journey.
Created by: Qi Zhao • zhaoq23009@gmail.com
Last updated: February 09 2026
Student-Level Actions - Personalized Interventions
🔴 Critical Priority Students (Top 10)
| Student ID | Priority | Risk Score | Top Risk Factor | Recommended Action |
|---|---|---|---|---|
| #1384 | Critical | 99.88% | Tuition fees up to date | Immediate intervention recommended: contact within 48 hours |
| #2454 | Critical | 99.87% | Tuition fees up to date | Immediate intervention recommended: contact within 48 hours |
| #3883 | Critical | 99.79% | Tuition fees up to date | Immediate intervention recommended: contact within 48 hours |
| #1403 | Critical | 99.79% | Tuition fees up to date | Immediate intervention recommended: contact within 48 hours |
| #4191 | Critical | 99.75% | Tuition fees up to date | Immediate intervention recommended: contact within 48 hours |
| #888 | Critical | 99.75% | CR_Sem2 | Immediate intervention recommended: contact within 48 hours |
| #3476 | Critical | 99.75% | CR_Sem2 | Immediate intervention recommended: contact within 48 hours |
| #3961 | Critical | 99.75% | CR_Sem2 | Immediate intervention recommended: contact within 48 hours |
| #3987 | Critical | 99.74% | Tuition fees up to date | Immediate intervention recommended: contact within 48 hours |
| #2455 | Critical | 99.72% | CR_Sem2 | Immediate intervention recommended: contact within 48 hours |
🟡 Warning Priority Students (Top 10)
| Student ID | Priority | Risk Score | Top Risk Factor | Recommended Action |
|---|---|---|---|---|
| #1727 | Warning | 69.66% | Curricular units 2nd sem (approved) | Proactive monitoring recommended: monthly check-ins |
| #4174 | Warning | 69.04% | CR_Sem2 | Proactive monitoring recommended: monthly check-ins |
| #912 | Warning | 68.69% | CR_Sem1 | Proactive monitoring recommended: monthly check-ins |
| #1505 | Warning | 68.60% | Tuition fees up to date | Proactive monitoring recommended: monthly check-ins |
| #3829 | Warning | 68.36% | CR_Sem2 | Proactive monitoring recommended: monthly check-ins |
| #2842 | Warning | 68.22% | Tuition fees up to date | Proactive monitoring recommended: monthly check-ins |
| #283 | Warning | 67.53% | CR_Sem2 | Proactive monitoring recommended: monthly check-ins |
| #2694 | Warning | 67.27% | CR_Sem2 | Proactive monitoring recommended: monthly check-ins |
| #152 | Warning | 67.17% | Course | Proactive monitoring recommended: monthly check-ins |
| #3496 | Warning | 66.70% | Tuition fees up to date | Proactive monitoring recommended: monthly check-ins |
🟢 On Track Students (Top 10)
| Student ID | Priority | Risk Score | Top Risk Factor | Recommended Action |
|---|---|---|---|---|
| #1686 | On Track | 39.88% | CR_Sem1 | On track: maintain current support level |
| #207 | On Track | 39.80% | Mother's occupation | On track: maintain current support level |
| #243 | On Track | 39.66% | CR_Sem1 | On track: maintain current support level |
| #1605 | On Track | 39.42% | CR_Sem2 | On track: maintain current support level |
| #2670 | On Track | 39.11% | CR_Sem2 | On track: maintain current support level |
| #3750 | On Track | 38.99% | Course | On track: maintain current support level |
| #1416 | On Track | 38.89% | Course | On track: maintain current support level |
| #759 | On Track | 38.70% | Curricular units 2nd sem (approved) | On track: maintain current support level |
| #2247 | On Track | 38.62% | Delta_CR | On track: maintain current support level |
| #1961 | On Track | 38.51% | Tuition fees up to date | On track: maintain current support level |
What-If Analysis - Intervention Simulation
Sample Student: #1384 - Baseline Dropout Risk: 99.88%
| Scenario | Description | New Risk | Risk Reduction | Reduction % |
|---|---|---|---|---|
| Baseline | No intervention | 99.88% | — | — |
| Academic Support | Increase 2nd semester grade by +10 and evaluations by +5 | 99.86% | 0.02% | 0.0% |
| Financial Support | Set tuition fees up to date and clear debtor status | 99.72% | 0.16% | 0.2% |
| Socio-emotional Support | Increase stress/support-related features by 20% (proxy) | 99.89% | -0.01% | -0.0% |
Key Finding: The most effective intervention is
Financial Support,
reducing dropout risk by 0.16%.
Cohort-Level Actions - Resource Allocation
Course Risk Analysis
Courses ranked by average dropout risk of enrolled students.
| Rank | Course Code | Course Name | Students | Avg Risk | Priority | Recommended Action |
|---|---|---|---|---|---|---|
| #1 | 33 | Biofuel Production Technologies | 2 | 85.69% | Critical | Immediate intervention: assign dedicated advisor and increase TA support |
| #2 | 9119 | Informatics Engineering | 37 | 52.75% | High | Increase TA support and monitor weekly |
| #3 | 9991 | Management (evening) | 53 | 52.42% | High | Increase TA support and monitor weekly |
| #4 | 9003 | Agronomy | 39 | 47.49% | High | Increase TA support and monitor weekly |
| #5 | 9130 | Equinculture | 30 | 41.50% | High | Increase TA support and monitor weekly |
| #6 | 9853 | Basic Education | 37 | 40.07% | High | Increase TA support and monitor weekly |
| #7 | 9773 | Journalism and Communication | 70 | 37.34% | Medium | Monitor closely and provide supplemental resources |
| #8 | 8014 | Social Service (evening) | 50 | 37.26% | Medium | Monitor closely and provide supplemental resources |
| #9 | 171 | Animation and Multimedia Design | 45 | 36.99% | Medium | Monitor closely and provide supplemental resources |
| #10 | 9556 | Oral Hygiene | 15 | 35.38% | Medium | Monitor closely and provide supplemental resources |
Methodology: Average dropout risk calculated as mean predicted probability across all students.
Cohort Comparison - Radar Chart Analysis
With Scholarship vs Without Scholarship (5 Dimensions)
| Dimension | With Scholarship | Without Scholarship | Difference |
|---|---|---|---|
| Academic Prep | 0.38 | 0.36 | +0.02 |
| Current Success | 0.47 | 0.37 | +0.10 |
| Engagement | 0.26 | 0.26 | 0.00 |
| Financial Stress | 0.95 | 0.89 | +0.06 |
| Stability | 0.22 | 0.29 | -0.07 |
Interpretation: Students With Scholarship show stronger Current Success (Δ=0.10)
Group 1 vs Group 0 (5 Dimensions)
| Dimension | Group 1 | Group 0 | Difference |
|---|---|---|---|
| Academic Prep | 0.36 | 0.37 | -0.01 |
| Current Success | 0.34 | 0.43 | -0.09 |
| Engagement | 0.25 | 0.26 | -0.01 |
| Financial Stress | 0.88 | 0.91 | -0.03 |
| Stability | 0.31 | 0.25 | +0.06 |
Interpretation: Students Group 0 show stronger Current Success (Δ=-0.09)
Group 1 vs Group 0 (5 Dimensions)
| Dimension | Group 1 | Group 0 | Difference |
|---|---|---|---|
| Academic Prep | 0.39 | 0.37 | +0.02 |
| Current Success | 0.40 | 0.40 | 0.00 |
| Engagement | 0.26 | 0.26 | 0.00 |
| Financial Stress | 0.90 | 0.90 | 0.00 |
| Stability | 0.29 | 0.27 | +0.02 |
Interpretation: Students Group 1 show stronger Academic Prep (Δ=0.02)
Entity-Driven Feedback Matrix
Natural Language Processing Analysis: Student feedback surveys analyzed using entity extraction and sentiment classification to identify actionable improvement areas.
Quadrant Interpretation:
- Success Cases (Top Right): High-frequency, positive topics - scale these practices
- Critical Issues (Bottom Right): High-frequency, negative topics - immediate intervention required
- Niche Strengths (Top Left): Low-frequency, positive topics - monitor and expand
- Minor Frustrations (Bottom Left): Low-frequency, negative topics - low priority
Key Action: Prioritize "Math Homework Volume" and
"Lab Report Guidelines" for immediate review, while expanding successful
practices like "Group Discussions" to other courses.
System-Level Actions - Policy Interventions
Top 10 Global Risk Factors
Features with the highest mean absolute SHAP values across all students.
| Rank | Feature | Mean |SHAP| | Policy Action |
|---|---|---|---|
| 1 | CR_Sem2 |
|
Monitor the factor and evaluate targeted interventions |
| 2 | Tuition fees up to date |
|
Establish emergency financial aid funding and improve payment flexibility |
| 3 | Curricular units 2nd sem (approved) |
|
Strengthen academic support programs and targeted tutoring |
| 4 | CR_Sem1 |
|
Monitor the factor and evaluate targeted interventions |
| 5 | Course |
|
Monitor the factor and evaluate targeted interventions |
| 6 | Unemployment rate |
|
Monitor the factor and evaluate targeted interventions |
| 7 | Curricular units 2nd sem (grade) |
|
Strengthen academic support programs and targeted tutoring |
| 8 | Gender |
|
Monitor the factor and evaluate targeted interventions |
| 9 | Age at enrollment |
|
Monitor the factor and evaluate targeted interventions |
| 10 | Admission grade |
|
Strengthen academic support programs and targeted tutoring |
Interpretation: These features represent institution-wide patterns that require systemic interventions.
Longitudinal Monitoring of Student Climate
NLP-Powered Sentiment Analysis & Entity Attribution
Automated Monitoring System: Monthly sentiment scores derived from student communications (emails, forum posts, surveys) with spaCy-powered entity extraction to identify structural friction points.
Average Sentiment
68.1
Academic Year 2024-25
Alert Triggers
2
Below Threshold (50)
Top Entity (Dec)
LMS Update
-15 point drop
Top Entity (Apr)
Regents Exam
-11 point drop
December 2024 Alert: Sentiment dropped to 45 following LMS system update.
Analysis of student communications revealed widespread login issues and lost assignment data.
Action Taken: Emergency technical support hours and deadline extensions implemented.
April 2025 Alert: Sentiment declined to 40 during Regents Exam period.
Entity analysis showed concerns about exam format changes and study resource availability.
Recommendation: Improve exam preparation communication and expand review sessions.
Methodology: Sentiment scores computed using transformer-based models (DistilBERT)
on student text data. Named entity recognition (spaCy) extracts topics/events during negative spikes.
System auto-generates alerts when sentiment drops below baseline for two consecutive data points.
Policy Simulation - Cost-Benefit Analysis
Baseline Metrics
Total Students
885
High-Risk Students
204
Baseline Dropout Rate
27.2%
Intervention Scenarios
Financial Aid Program
Description: Clear debt for 44 high-risk students with debt (subset of 204 high-risk students)
Beneficiaries: 44 students
Total Cost: $88,000
Dropouts Prevented: 13
Cost per Student Saved: $6,769
New Dropout Rate: 25.76%
Engagement Nudge System
Description: Proxy boosts to engagement/achievement features (+30%) for all 204 high-risk students
Beneficiaries: 204 students
Total Cost: $500
Dropouts Prevented: 2
Cost per Student Saved: $250
New Dropout Rate: 27.01%
Recommendation: Prioritize interventions with lowest cost per student saved.
Intake & Quality Checks
Weekly intake → schema alignment → integrity audit → target balance & drift
Basic checks
| rows | cols | missing_cells | missing_cell_pct | duplicate_rows | has_missing | has_duplicates |
|---|---|---|---|---|---|---|
| 4424 | 37 | 0 | 0.0 | 0 | No | No |
Column type summary
| dtype | n_cols |
|---|---|
| Int64 | 29 |
| float64 | 7 |
| str | 1 |
Details (click to expand):
Int64 — 29 columns
Marital status, Application mode, Application order, Course, Daytime/evening attendance, Previous qualification, Nacionality, Mother's qualification, Father's qualification, Mother's occupation, Father's occupation, Displaced, Educational special needs, Debtor, Tuition fees up to date, Gender, Scholarship holder, Age at enrollment, International, Curricular units 1st sem (credited), Curricular units 1st sem (enrolled), Curricular units 1st sem (evaluations), Curricular units 1st sem (approved), Curricular units 1st sem (without evaluations), Curricular units 2nd sem (credited), Curricular units 2nd sem (enrolled), Curricular units 2nd sem (evaluations), Curricular units 2nd sem (approved), Curricular units 2nd sem (without evaluations)
float64 — 7 columns
Previous qualification (grade), Admission grade, Curricular units 1st sem (grade), Curricular units 2nd sem (grade), Unemployment rate, Inflation rate, GDP
str — 1 columns
Target
Outcome distribution
No data available.
Data Quality Checks
1. Missing Data Check
No missing data issues found.
2. Outlier Check
| column | outlier_count | outlier_pct | handling | rationale |
|---|---|---|---|---|
| Age at enrollment | 156 | 3.526221 | flag_only | High age represents non-traditional students (Risk Signal) |
| Curricular units 1st sem (evaluations) | 33 | 0.745931 | flag_only | Default: Flag for monitoring without modification |
| Curricular units 2nd sem (evaluations) | 15 | 0.339060 | flag_only | Default: Flag for monitoring without modification |
3. Sanity Check (Integrity Audit)
Info (Review)
| check_name | severity | affected_rows | details |
|---|---|---|---|
| tuition_vs_scholarship_review | INFO | 46 | Scholarship=1 but tuition=0. Review: partial scholarship or payment timing? |
Basic checks
| rows | cols | missing_cells | missing_cell_pct | duplicate_rows | has_missing | has_duplicates |
|---|---|---|---|---|---|---|
| 1475 | 37 | 0 | 0.0 | 0 | No | No |
Column type summary
| dtype | n_cols |
|---|---|
| Int64 | 29 |
| float64 | 7 |
| str | 1 |
Details (click to expand):
Int64 — 29 columns
Marital status, Application mode, Application order, Course, Daytime/evening attendance, Previous qualification, Nacionality, Mother's qualification, Father's qualification, Mother's occupation, Father's occupation, Displaced, Educational special needs, Debtor, Tuition fees up to date, Gender, Scholarship holder, Age at enrollment, International, Curricular units 1st sem (credited), Curricular units 1st sem (enrolled), Curricular units 1st sem (evaluations), Curricular units 1st sem (approved), Curricular units 1st sem (without evaluations), Curricular units 2nd sem (credited), Curricular units 2nd sem (enrolled), Curricular units 2nd sem (evaluations), Curricular units 2nd sem (approved), Curricular units 2nd sem (without evaluations)
float64 — 7 columns
Previous qualification (grade), Admission grade, Curricular units 1st sem (grade), Curricular units 2nd sem (grade), Unemployment rate, Inflation rate, GDP
str — 1 columns
Target
Outcome distribution
No data available.
Data Quality Checks
1. Missing Data Check
No missing data issues found (or see Overall tab).
2. Outlier Check
No outlier issues found (or see Overall tab).
3. Sanity Check (Integrity Audit)
Warnings (WARN)
| check_name | severity | affected_rows | details |
|---|---|---|---|
| gdp_negative | WARN | 596 | GDP < 0 (data quality issue) |
| inflation_negative | WARN | 307 | Inflation rate < 0 (deflation or data issue) |
Info (Review)
| check_name | severity | affected_rows | details |
|---|---|---|---|
| tuition_vs_scholarship_review | INFO | 19 | Scholarship=1 but tuition=0. Review: partial scholarship or payment timing? |
Basic checks
| rows | cols | missing_cells | missing_cell_pct | duplicate_rows | has_missing | has_duplicates |
|---|---|---|---|---|---|---|
| 1475 | 37 | 0 | 0.0 | 0 | No | No |
Column type summary
| dtype | n_cols |
|---|---|
| Int64 | 29 |
| float64 | 7 |
| str | 1 |
Details (click to expand):
Int64 — 29 columns
Marital status, Application mode, Application order, Course, Daytime/evening attendance, Previous qualification, Nacionality, Mother's qualification, Father's qualification, Mother's occupation, Father's occupation, Displaced, Educational special needs, Debtor, Tuition fees up to date, Gender, Scholarship holder, Age at enrollment, International, Curricular units 1st sem (credited), Curricular units 1st sem (enrolled), Curricular units 1st sem (evaluations), Curricular units 1st sem (approved), Curricular units 1st sem (without evaluations), Curricular units 2nd sem (credited), Curricular units 2nd sem (enrolled), Curricular units 2nd sem (evaluations), Curricular units 2nd sem (approved), Curricular units 2nd sem (without evaluations)
float64 — 7 columns
Previous qualification (grade), Admission grade, Curricular units 1st sem (grade), Curricular units 2nd sem (grade), Unemployment rate, Inflation rate, GDP
str — 1 columns
Target
Outcome distribution
No data available.
Drift check
This vs last
| Target | this_pct | last_pct | delta_pct |
|---|---|---|---|
| Graduate | 49.762712 | 48.474576 | 1.288136 |
| Dropout | 31.661017 | 33.152542 | -1.491525 |
| Enrolled | 18.576271 | 18.372881 | 0.203390 |
This vs cumulative
| Target | this_pct | week1_pct | delta_pct |
|---|---|---|---|
| Graduate | 49.762712 | 48.474576 | 1.288136 |
| Dropout | 31.661017 | 33.152542 | -1.491525 |
| Enrolled | 18.576271 | 18.372881 | 0.203390 |
Data Quality Checks
1. Missing Data Check
No missing data issues found (or see Overall tab).
2. Outlier Check
No outlier issues found (or see Overall tab).
3. Sanity Check (Integrity Audit)
Warnings (WARN)
| check_name | severity | affected_rows | details |
|---|---|---|---|
| gdp_negative | WARN | 572 | GDP < 0 (data quality issue) |
| inflation_negative | WARN | 316 | Inflation rate < 0 (deflation or data issue) |
Info (Review)
| check_name | severity | affected_rows | details |
|---|---|---|---|
| tuition_vs_scholarship_review | INFO | 14 | Scholarship=1 but tuition=0. Review: partial scholarship or payment timing? |
Basic checks
| rows | cols | missing_cells | missing_cell_pct | duplicate_rows | has_missing | has_duplicates |
|---|---|---|---|---|---|---|
| 1474 | 37 | 0 | 0.0 | 0 | No | No |
Column type summary
| dtype | n_cols |
|---|---|
| Int64 | 29 |
| float64 | 7 |
| str | 1 |
Details (click to expand):
Int64 — 29 columns
Marital status, Application mode, Application order, Course, Daytime/evening attendance, Previous qualification, Nacionality, Mother's qualification, Father's qualification, Mother's occupation, Father's occupation, Displaced, Educational special needs, Debtor, Tuition fees up to date, Gender, Scholarship holder, Age at enrollment, International, Curricular units 1st sem (credited), Curricular units 1st sem (enrolled), Curricular units 1st sem (evaluations), Curricular units 1st sem (approved), Curricular units 1st sem (without evaluations), Curricular units 2nd sem (credited), Curricular units 2nd sem (enrolled), Curricular units 2nd sem (evaluations), Curricular units 2nd sem (approved), Curricular units 2nd sem (without evaluations)
float64 — 7 columns
Previous qualification (grade), Admission grade, Curricular units 1st sem (grade), Curricular units 2nd sem (grade), Unemployment rate, Inflation rate, GDP
str — 1 columns
Target
Outcome distribution
No data available.
Drift check
This vs last
| Target | this_pct | last_pct | delta_pct |
|---|---|---|---|
| Graduate | 51.560380 | 49.762712 | 1.797668 |
| Dropout | 31.546811 | 31.661017 | -0.114206 |
| Enrolled | 16.892809 | 18.576271 | -1.683463 |
This vs cumulative
| Target | this_pct | cum(w1+w2)_pct | delta_pct |
|---|---|---|---|
| Graduate | 51.560380 | 49.118644 | 2.441736 |
| Dropout | 31.546811 | 32.406780 | -0.859968 |
| Enrolled | 16.892809 | 18.474576 | -1.581768 |
Data Quality Checks
1. Missing Data Check
No missing data issues found (or see Overall tab).
2. Outlier Check
No outlier issues found (or see Overall tab).
3. Sanity Check (Integrity Audit)
Warnings (WARN)
| check_name | severity | affected_rows | details |
|---|---|---|---|
| gdp_negative | WARN | 543 | GDP < 0 (data quality issue) |
| inflation_negative | WARN | 300 | Inflation rate < 0 (deflation or data issue) |
Info (Review)
| check_name | severity | affected_rows | details |
|---|---|---|---|
| tuition_vs_scholarship_review | INFO | 13 | Scholarship=1 but tuition=0. Review: partial scholarship or payment timing? |
Feature Profiling
Variable distributions by feature group (binary, continuous, categorical)
Data Dictionary: For coded variables, see UCI Dataset Documentation ↗
Demographics
Binary variables
Continuous/Count variables
Categorical variables
Family background
Categorical variables
Financial / administrative
Binary variables
Admissions & pathway
Continuous/Count variables
Categorical variables
Academic signals (Sem 1)
Continuous/Count variables
Academic signals (Sem 2)
Continuous/Count variables
Macro context
Continuous/Count variables
Target Analysis
How different variables relate to outcomes (Graduate / Enrolled / Dropout)
Gender
Scholarship holder
Tuition fees up to date
Debtor
International
Age at enrollment
Admission grade
Curricular units 1st sem (approved)
Curricular units 1st sem (grade)
Feature Engineering
Temporal trajectories and text-derived features
Longitudinal Features
Time-series features capturing student trajectory over semesters
| Feature Name | Description | Type | Mean |
|---|---|---|---|
| Delta_Grade | Change in average grade from Semester 1 to Semester 2 | Continuous | -0.411 |
| Grade_Improvement | Binary indicator of grade improvement | Binary | 0.351 |
| Grade_Decline | Binary indicator of grade decline | Binary | 0.389 |
| CR_Sem1 | Credit Ratio Semester 1 (approved/enrolled) | Continuous | 0.727 |
| CR_Sem2 | Credit Ratio Semester 2 (approved/enrolled) | Continuous | 0.688 |
| Delta_CR | Change in Credit Ratio from Semester 1 to Semester 2 | Continuous | -0.039 |
| Completion_Collapse | Binary indicator of completion rate collapse | Binary | 0.022 |
| Fail_Crossing | Binary indicator of crossing the failure threshold | Binary | 0.044 |
| Borderline_Collapse | Binary indicator of borderline performance collapse | Binary | 0.017 |
| EvalPressure_Sem1 | Evaluation pressure Semester 1 (evaluations/enrolled) | Continuous | 1.345 |
| EvalPressure_Sem2 | Evaluation pressure Semester 2 (evaluations/enrolled) | Continuous | 1.315 |
| Delta_EvalPressure | Change in evaluation pressure between semesters | Continuous | -0.030 |
| GhostRate_Sem1 | Ghost enrollment rate Semester 1 | Continuous | 0.023 |
| GhostRate_Sem2 | Ghost enrollment rate Semester 2 | Continuous | 0.026 |
| Delta_GhostRate | Change in ghost enrollment rate | Continuous | 0.003 |
| Ghost_Worsening | N/A | Binary | 0.011 |
Key Insight: Longitudinal features (especially Delta_CR and CR_Sem2)
often capture whether performance is improving or declining over time.
Delta-based features can add signal beyond point-in-time snapshots by reflecting trajectory and momentum.
NLP Features (Simulated)
Text-derived psychological and behavioral signals
| Feature Name | Description | Source | Coverage | Mean |
|---|---|---|---|---|
| Academic_Stress | Stress level indicator derived from sentiment analysis | Sentiment analysis | 70.8% | 0.206 |
| Academic_Stress_Level | Categorical stress level (Low/Medium/High) | Sentiment analysis | 70.8% | N/A |
| Home_Support_Risk | Family support risk score (0=stable, 1=at-risk) | Topic modeling | 70.8% | 0.215 |
| Subject_Specific | Subject-specific difficulties identified | Keyword extraction | 100.0% | N/A |
| Subject_Risk_Flag | Binary indicator of subject-specific difficulty | Keyword extraction | 100.0% | 0.328 |
| Subject_Difficulty_Score | Numerical difficulty score for subject | Keyword extraction | 70.8% | 0.340 |
Academic Stress Level Distribution
Sample Simulated Texts
Student 0:
"I'm enjoying my courses and managing time well. English writing assignments are very difficult."
Stress: Low Home Risk: Medium Subject: Language
Student 3:
"I have good support from family and doing well. Language barrier is making comprehension hard."
Stress: Low Home Risk: Low Subject: Language
Student 4:
"Some courses are harder than expected, but I'm coping. English writing assignments are very difficult."
Stress: Medium Home Risk: Low Subject: Language
Note: These features are simulated for demonstration.
A production implementation would use actual student text data.
Correlation Analysis
Examining relationships between different feature types
Type 1: Static Variables
Correlations among demographic and enrollment features
Interpretation: Static features often show modest correlations.
Typical patterns include relationships among age, admission grade, and prior qualifications.
Type 2: Static vs Longitudinal
How baseline characteristics relate to performance trajectories
Interpretation: Baseline demographic features often have weak-to-moderate
correlations with longitudinal change features, suggesting that semester-to-semester dynamics
are not fully explained by starting characteristics alone.
Type 3: Longitudinal vs NLP
Links between performance metrics and psychological indicators
Interpretation: Stress-related features may be negatively related to credit ratios,
while home-support risk can align with indicators of academic decline, depending on how the simulated
signals were generated.
Feature Importance
Identifying predictive features using multiple methods
L1 Logistic Regression
Sparse feature selection through L1 regularization
| Rank | Feature | Coefficient |
|---|---|---|
| 1 | CR_Sem2 | -1.171008 |
| 2 | Tuition fees up to date | -0.726209 |
| 3 | Curricular units 1st sem (approved) | -0.604850 |
| 4 | Curricular units 2nd sem (credited) | 0.317461 |
| 5 | Curricular units 2nd sem (approved) | -0.305634 |
| 6 | Age at enrollment | 0.258476 |
| 7 | Mother's occupation | -0.233480 |
| 8 | Scholarship holder | -0.219680 |
| 9 | Curricular units 1st sem (credited) | 0.207680 |
| 10 | International | -0.168801 |
Interpretation: L1 regularization performs feature selection by shrinking
less informative coefficients toward zero. Features with non-zero coefficients carry the strongest signal.
Random Forest
Feature importance based on impurity reduction
| Rank | Feature | Importance |
|---|---|---|
| 1 | CR_Sem2 | 0.165800 |
| 2 | CR_Sem1 | 0.115122 |
| 3 | Curricular units 2nd sem (approved) | 0.111475 |
| 4 | Curricular units 2nd sem (grade) | 0.098250 |
| 5 | Tuition fees up to date | 0.063876 |
| 6 | Curricular units 1st sem (approved) | 0.057655 |
| 7 | Curricular units 1st sem (grade) | 0.040976 |
| 8 | Academic_Stress | 0.034186 |
| 9 | Age at enrollment | 0.025320 |
| 10 | Delta_CR | 0.020882 |
Interpretation: Random Forest importance reflects how much each feature reduces
prediction uncertainty across splits. This method can capture non-linear relationships.
Combined Ranking
Consensus ranking across L1 and Random Forest methods
| Feature | Coefficient | Importance | Combined_Score | L1_Rank | RF_Rank |
|---|---|---|---|---|---|
| CR_Sem2 | -1.171008 | 0.165800 | 5.937941 | 1.0 | 1.0 |
| Tuition fees up to date | -0.726209 | 0.063876 | 3.662985 | 2.0 | 5.0 |
| Curricular units 1st sem (approved) | -0.604850 | 0.057655 | 3.053077 | 3.0 | 6.0 |
| Curricular units 2nd sem (credited) | 0.317461 | 0.003135 | 1.588870 | 4.0 | 36.0 |
| Curricular units 2nd sem (approved) | -0.305634 | 0.111475 | 1.583906 | 5.0 | 3.0 |
| Age at enrollment | 0.258476 | 0.025320 | 1.305040 | 6.0 | 9.0 |
| Mother's occupation | -0.233480 | 0.008997 | 1.171901 | 7.0 | 22.0 |
| Scholarship holder | -0.219680 | 0.005523 | 1.101163 | 8.0 | 28.0 |
| Curricular units 1st sem (credited) | 0.207680 | 0.004117 | 1.040456 | 9.0 | 32.0 |
| International | -0.168801 | 0.000000 | 0.844006 | 10.0 | 51.0 |
Summary: Features that rank highly across both methods are typically the most stable
candidates for downstream decision support and intervention design.
Model Overview
Dataset summary and modeling approach
Dataset Statistics
Total Samples
4424
Training Set
3539
Test Set
885
Features
61
Target Distribution
| Outcome | Count | Percentage |
|---|---|---|
| Graduate | 2209 | 49.9% |
| Dropout | 1421 | 32.1% |
| Enrolled | 794 | 17.9% |
Modeling Strategy: Multi-class classification with balanced class weights to address class imbalance.
Class Imbalance Analysis
Light Imbalance - Class ratio: 2.78:1
(Largest class: Graduate = 2,209, Smallest class: Enrolled = 794)
Handling Strategy
| Strategy | Rationale |
|---|---|
| class_weight_only |
With a class ratio of 2.78:1, a conservative class_weight="balanced"
approach is a reasonable baseline. This adjusts the loss contribution by class without modifying
the observed data distribution.
|
Best Model Selection
Model
XGBoost
ROC-AUC (Macro)
0.886
| Attribute | Details |
|---|---|
| Purpose | Comparison benchmark |
| Regularization Strategy | L1/L2 regularization + tree constraints |
Selection Criteria: Best model selected based on macro-averaged ROC-AUC and F1-score,
balancing performance across the three outcome classes.
Model Performance Comparison
| Model | Purpose | Precision | Recall | F1-Score | ROC-AUC |
|---|---|---|---|---|---|
| Logistic Regression (L1) | Interpretability baseline - identify core risk factors | 0.629 | 0.627 | 0.622 | 0.809 |
| Decision Tree | Rule extraction - interpretable decision logic for stakeholders | 0.668 | 0.634 | 0.635 | 0.840 |
| Random Forest | Performance benchmark - candidate primary model | 0.703 | 0.705 | 0.694 | 0.875 |
| XGBoost | Comparison benchmark | 0.705 | 0.682 | 0.689 | 0.886 |
Model Selection: Best model selected based on macro-averaged F1-score and ROC-AUC.
Detailed Model Information
Logistic Regression (L1)
Purpose: Interpretability baseline - identify core risk factors
Regularization: L1 penalty (C=0.1) + balanced class weights
Regularization: L1 penalty (C=0.1) + balanced class weights
Confusion Matrix
| Pred: Graduate | Pred: Dropout | Pred: Enrolled | |
|---|---|---|---|
| True: Graduate | 175 | 73 | 36 |
| True: Dropout | 32 | 79 | 48 |
| True: Enrolled | 32 | 71 | 339 |
Top 10 Feature Importance
| Rank | Feature | Importance |
|---|---|---|
| 1 | Curricular units 2nd sem (grade) | 0.0480 |
| 2 | Curricular units 2nd sem (approved) | 0.0368 |
| 3 | Curricular units 1st sem (grade) | 0.0306 |
| 4 | Curricular units 1st sem (approved) | 0.0302 |
| 5 | Curricular units 2nd sem (evaluations) | 0.0210 |
| 6 | Age at enrollment | 0.0204 |
| 7 | Delta_Grade | 0.0174 |
| 8 | Curricular units 1st sem (evaluations) | 0.0153 |
| 9 | Application mode | 0.0067 |
| 10 | Unemployment rate | 0.0066 |
Decision Tree
Purpose: Rule extraction - interpretable decision logic for stakeholders
Regularization: Max depth=5, min samples leaf=50
Regularization: Max depth=5, min samples leaf=50
Confusion Matrix
| Pred: Graduate | Pred: Dropout | Pred: Enrolled | |
|---|---|---|---|
| True: Graduate | 172 | 75 | 37 |
| True: Dropout | 19 | 80 | 60 |
| True: Enrolled | 1 | 91 | 350 |
Top 10 Feature Importance
| Rank | Feature | Importance |
|---|---|---|
| 1 | CR_Sem2 | 0.7330 |
| 2 | Tuition fees up to date | 0.0896 |
| 3 | Delta_CR | 0.0717 |
| 4 | CR_Sem1 | 0.0364 |
| 5 | Curricular units 2nd sem (evaluations) | 0.0201 |
| 6 | GDP | 0.0127 |
| 7 | Age at enrollment | 0.0111 |
| 8 | EvalPressure_Sem1 | 0.0096 |
| 9 | Delta_EvalPressure | 0.0048 |
| 10 | Course | 0.0042 |
Random Forest
Purpose: Performance benchmark - candidate primary model
Regularization: Balanced class weights + depth and leaf constraints
Regularization: Balanced class weights + depth and leaf constraints
Confusion Matrix
| Pred: Graduate | Pred: Dropout | Pred: Enrolled | |
|---|---|---|---|
| True: Graduate | 201 | 64 | 19 |
| True: Dropout | 26 | 98 | 35 |
| True: Enrolled | 13 | 80 | 349 |
Top 10 Feature Importance
| Rank | Feature | Importance |
|---|---|---|
| 1 | CR_Sem2 | 0.1565 |
| 2 | Curricular units 2nd sem (approved) | 0.1390 |
| 3 | CR_Sem1 | 0.1200 |
| 4 | Curricular units 2nd sem (grade) | 0.0775 |
| 5 | Curricular units 1st sem (approved) | 0.0750 |
| 6 | Tuition fees up to date | 0.0464 |
| 7 | EvalPressure_Sem2 | 0.0393 |
| 8 | Curricular units 1st sem (grade) | 0.0372 |
| 9 | Academic_Stress | 0.0352 |
| 10 | EvalPressure_Sem1 | 0.0341 |
Fairness Audit Overview
Features Audited
7
DI Pass Rate
100%
EO Pass Rate
100%
Overall Status
Pass
Fairness Criteria: Disparate Impact ≥ 0.8 and Equalized Odds differences < 0.1.
Feature: Gender
| Metric | Result | Status |
|---|---|---|
| Disparate Impact | 1.000 | Fair |
| TPR Difference | 0.000 | Fair |
| FPR Difference | 0.000 | Fair |
Feature: Scholarship holder
| Metric | Result | Status |
|---|---|---|
| Disparate Impact | 1.000 | Fair |
| TPR Difference | 0.000 | Fair |
| FPR Difference | 0.000 | Fair |
Feature: Displaced
| Metric | Result | Status |
|---|---|---|
| Disparate Impact | 1.000 | Fair |
| TPR Difference | 0.000 | Fair |
| FPR Difference | 0.000 | Fair |
Feature: International
| Metric | Result | Status |
|---|---|---|
| Disparate Impact | 1.000 | Fair |
| TPR Difference | 0.000 | Fair |
| FPR Difference | 0.000 | Fair |
Feature: Nacionality
| Metric | Result | Status |
|---|---|---|
| Disparate Impact | 1.000 | Fair |
| TPR Difference | 0.000 | Fair |
| FPR Difference | 0.000 | Fair |
Feature: Debtor
| Metric | Result | Status |
|---|---|---|
| Disparate Impact | 1.000 | Fair |
| TPR Difference | 0.000 | Fair |
| FPR Difference | 0.000 | Fair |
Feature: Educational special needs
| Metric | Result | Status |
|---|---|---|
| Disparate Impact | 1.000 | Fair |
| TPR Difference | 0.000 | Fair |
| FPR Difference | 0.000 | Fair |